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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode

Huichao Yan1,2, Ting Xu3, Peng Wang4

  • 1School of Information and Communication Engineering, North University of China, Taiyuan 030051, China. b1705012@st.nuc.edu.cn.

Sensors (Basel, Switzerland)
|October 27, 2019
PubMed
Summary

This study introduces a novel algorithm for denoising and removing baseline drift in MEMS vector hydrophone signals. The method utilizes whale-optimized variational mode decomposition (VMD) and correlation coefficients (CC) for enhanced underwater acoustic data processing.

Keywords:
baseline drift removalcross correlation (CC)denoisingpower spectrum entropy (PSE)variational mode decomposition (VMD)whale-optimization algorithm (WOA)

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Area of Science:

  • Oceanography
  • Signal Processing
  • Acoustic Engineering

Background:

  • Underwater acoustic technology is crucial for ocean detection.
  • Marine environments introduce significant noise and baseline drift in hydrophone signals.
  • Existing methods struggle with effective denoising and baseline drift removal in complex acoustic data.

Purpose of the Study:

  • To develop an advanced algorithm for denoising and baseline drift removal in MEMS vector hydrophone signals.
  • To improve the accuracy and reliability of underwater acoustic data acquisition.
  • To address the limitations of conventional signal processing techniques in noisy marine environments.

Main Methods:

  • Proposed a denoising and baseline drift removal algorithm for MEMS vector hydrophones.
  • Employed whale-optimized variational mode decomposition (VMD) with power spectrum entropy (PSE) as the fitness function to determine optimal VMD parameters (K, α).
  • Utilized correlation coefficients (CC) to identify and remove noise-intrinsic mode functions (IMFs), followed by reconstruction of useful IMFs.

Main Results:

  • The proposed algorithm demonstrated superior performance in denoising and baseline drift removal compared to conventional methods in simulations.
  • Effectiveness was validated through experiments with a MEMS hydrophone, confirming its practical applicability.
  • The algorithm successfully reconstructed signals by selectively filtering noise-dominant IMFs.

Conclusions:

  • The whale-optimized VMD and CC-based algorithm offers a robust solution for underwater acoustic signal processing.
  • This method provides a significant advancement for MEMS vector hydrophone data analysis.
  • The findings offer new avenues for signal denoising and baseline drift removal in challenging acoustic environments.